Comparing satellite and helicopter-based methods for observing crevasses, application in East Antarctica

Knowing where crevasses are is critical for planning safe on-ice field operations. Previous methods have ranged from real-time imaging of subsurface structures using ground penetrating radar, to mapping of crevasses over large areas using satellite imagery, with each method having it\u27s own strengths and weaknesses. In this paper we compare the detection of crevasses at the Totten Glacier, East Antarctica, from helicopter-borne ground penetrating radar with satellite-based microwave synthetic aperture radar imagery. Our results show that the 80 MHz helicopter-borne ground penetrating radar was able to detect crevasses up to a depth of 70 m, with snow bridge thickness of \u3e30 m. Comparison with TerraSAR-X (X-band, 9.6 GHz) satellite imagery indicates that the latter is highly effective, detecting 100% of crevasses with snow bridges of up to 4m thick and detecting 95% of crevasses with snow bridges up to 10 m thick. The ability of both methods to identify individual crevasses is affected by several factors including crevasse geometry, survey or satellite orientation and snow moisture content, and further experiments are planned to investigate performance under a wider range of conditions

HF Wire-Mesh Dipole Antennas for Broadband Ice-Penetrating Radar

In this letter, a novel high-frequency to very-high-frequency wire-mesh dipole antenna design for use in polar regions is discussed and evaluated. The antenna was designed to be lightweight, readily demountable, and acceptably robust. This is an initial step in the development of a ground-based, phase-sensitive, synthetic aperture imaging system for use on an autonomous rover platform. The results of initial trials on the Rhône Glacier, Switzerland, in August 2019, are discussed, with particular attention being paid to the effect on the antenna performance of high surface water content. Including the effects of the surface water resulted in good agreement between field results, and modeled performance

A low-cost autonomous rover for polar science

We present the developmental considerations, design, and deployment of an autonomous modular terrestrial rover for ice-sheet exploration that is inexpensive, easy to construct, and allows for instrumentation customization. The total construction cost for this rover is less than USD 3000, approximately one-tenth the cost of existing platforms, and it can be built using facilities frequently available at academic institutions (machine shop, 3-D printer, open-source hardware and software). Instrumentation deployed on this rover can be customized; the rover presented in this study was equipped with a dual-frequency GPS receiver and a digital SLR camera for constructing digital elevation models using structure-from-motion (SfM) photogrammetry. We deployed this prototype rover on the Northeast Greenland Ice Stream to map local variations in snow accumulation and surface topography. The rover conducted four autonomous missions based out of the East Greenland Ice-Core Project (EastGRIP) camp during July 2017, measuring surface elevation transects across the hazardous ice-stream shear margins. During these missions, the rover proved capable of driving over 20 km on a single charge with a drawbar pull of 250 N, sufficient to tow instrumentation of up to 100 kg. The rover also acquired photographs that were subsequently used to construct digital elevation models of a site monitored for spatiotemporal variability in snow accumulation, demonstrating adequate stability for high-resolution imaging applications. Due to its low cost, low-power requirements, and simple modular design, mass deployments of this rover design are practicable. Operation of the rover in hazardous areas circumvents the substantial expense and risk to personnel associated with conventional, crewed deployments. Thus, this rover is an investigatory platform that enables direct exploration of polar environments considered too hazardous for conventional field expeditions.publishedVersio

Analysis of error functions for the iterative closest point algorithm

Dans les dernières années, beaucoup de progrès a été fait dans le domaine des voitures autonomes. Plusieurs grandes compagnies travaillent à créer un véhicule robuste et sûr. Pour réaliser cette tâche, ces voitures utilisent un lidar pour la localisation et pour la cartographie. Iterative Closest Point (ICP)est un algorithme de recalage de points utilisé pour la cartographie basé sur les lidars. Ce mémoire explore des approches pour améliorer le minimisateur d’erreur d’ICP. La première approche est une analyse en profondeur des filtres à données aberrantes. Quatorze des filtres les plus communs (incluant les M-estimateurs) ont été testés dans différents types d’environnement, pour un total de plus de 2 millions de recalages. Les résultats expérimentaux montrent que la plupart des filtres ont des performances similaires, s’ils sont correctement paramétrés. Néanmoins, les filtres comme Var.Trim., Cauchy et Cauchy MAD sont plus stables à travers tous les types environnements testés. La deuxième approche explore les possibilités de la cartographie à grande échelle à l’aide de lidar dans la forêt boréale. La cartographie avec un lidar est souvent basée sur des techniques de Simultaneous Localization and Mapping (SLAM) utilisant un graphe de poses, celui-ci fusionne ensemble ICP, les positions Global Navigation Satellite System (GNSS) et les mesures de l’Inertial Measurement Unit (IMU). Nous proposons une approche alternative qui fusionne ses capteurs directement dans l’étape de minimisation d’ICP. Nous avons réussi à créer une carte ayant 4.1 km de tracés de motoneige et de chemins étroits. Cette carte est localement et globalement cohérente.In recent years a lot of progress has been made in the development of self-driving cars. Multiple big companies are working on creating a safe and robust autonomous vehicle . To make this task possible, theses vehicles rely on lidar sensors for localization and mapping. Iterative Closest Point (ICP) is a registration algorithm used in lidar-based mapping. This thesis explored approaches to improve the error minimization of ICP. The first approach is an in-depth analysis of outlier filters. Fourteen of the most common outlier filters (such as M-estimators) have been tested in different types of environments, for a total of more than two million registrations. The experimental results show that most outlier filters have a similar performance if they are correctly tuned. Nonetheless, filters such as Var.Trim., Cauchy, and Cauchy MAD are more stable against different environment types. The second approach explores the possibilities of large-scale lidar mapping in a boreal forest. Lidar mapping is often based on the SLAM technique relying on pose graph optimization, which fuses the ICP algorithm, GNSS positioning, and IMU measurements. To handle those sensors directly within theICP minimization process, we propose an alternative technique of embedding external constraints. We manage to create a crisp and globally consistent map of 4.1 km of snowmobile trails and narrow walkable trails. These two approaches show how ICP can be improved through the modification of a single step of the ICP’s pipeline

Characterization and Influence of the McMurdo Shear Zone, Antarctica on the Ross Ice Shelf and Its Tributary

As ice shelves are floating and lack resistive stress at their base, resistance to flow is accommodated along their lateral margins and various pinning points such as ice rises and nunataks. As such, ice shelf shear margins and their strength through time remain a critical control on ice shelf stability. Specifically, lateral shear zone destabilization is an important precursor to ice shelf collapse. In this thesis I utilize in-situ, remote sensing, and numerical modeling techniques in order to characterize the flow field and geometry of the western lateral margin of the Ross Ice Shelf as well as a region upstream of the grounding line. I first develop a method to investigate the kinematic drivers of crevasse initiation in the McMurdo Shear Zone, Antarctica through the delineation of crevasse features from ground penetrating radar observations and comparison with kinematic outputs derived from remotely-sensed ice surface velocities. I then use spatial patterns in crevasse attributes to make inferences on crevasse history and discuss implications on the current and future stability of the shear margin. Next, I estimate ice thickness within this shear margin from a combination of mid-frequency ground penetrating radar observations and Digital Elevation Models and assess the sensitivity of Ross Ice Shelf stress balance to uncertainties in ice thickness datasets within this region through numerical modeling techniques. My results suggest that previous modeling frameworks have overestimated the sensitivity of the region to melting. Finally, I perform a transient streamline analysis of a region of upstream grounded ice known as the Whillans and Ice Stream and characterize the short-term velocity fluctuations within its slowdown evolution using available remotely-sensed velocity datasets between 1997 and 2016. I incorporate these observations of velocity fluctuations as well as mass changes into a transient finite element modeling solution of ice flow. Through inversion techniques, I estimate annual changes in basal shear stress in order to force a 100-year transient model of ice slowdown for the Whillans Ice Stream and discuss the possible mechanisms that could be driving slowdown fluctuations on annual timescales such as lake drainage events, basal freeze-on and till weakening mechanisms, as well as changes to downstream boundary conditions